The Leading Data Platform for AI and Analytics

Faster, More Accurate Predictions. Simpler Development. All at a Lower TCO.

EBOOK

Machine Learning Logistics

When it comes to AI and ML, data scientists tend to focus on tools and algorithms. Though the data platform that enables these tools is equally important, it is often overlooked.

Data scientists using MapR for AI and ML can:

Get more accurate results by running any model against all data anywhere

Push the best models to production faster

Containerize AI and ML models and train them against all data without having to first move that data

Leverage open interfaces like POSIX, letting you use the best AI or ML tool for the job

Developers have no shortage of choices when it comes to analytical applications. Cloud vendors have recently become the go-to choice, but developers should always consider the cost of writing (or rewriting) applications against cloud- and other vendor-specific APIs.

Developers using MapR can:

Simplify development by writing against open APIs including JSON, S3, HDFS, and Kafka

Organizations recognize competitors are beginning to infuse AI and ML across business lines, but it's not always clear how to compete in this new terrain, especially when cost and security remain top of mind concerns.

CxOs leveraging MapR benefit from:

Lower TCO with data analyzed in-place and in one cluster, which can be automatically tiered to lower-cost storage based on usage

Out-of-box security managed at the platform level instead of by add-on software

Achieving the next level of AI and ML maturity with experienced MapR data scientists on-hand to help

Which AI or Analytics Initiative Will You Speed Up?

Organizations across a wide range of industries aim to increase their bottom line by reducing downtime and optimizing output. Predictive maintenance is gaining in popularity as a way to solve for this. With predictive maintenance, IoT- and sensor-enabled equipment emits data that can be used to forecast and ideally prevent failures. Yet the data required and the level of AI and ML modeling needed -- particularly at the edge -- often prevents organizations from reaping the full benefits of predictive maintenance.

MapR scales out to store, process, and analyze data at full fidelity, whether at the edge, on-premises, or in one or more clouds. In combination with AI and ML models that can be containerized and run against data across these disparate environments, MapR powers faster decision making and greater production success for organizations' predictive maintenance practices.

Across industries billions of dollars are lost annually to fraud, from fraudulent card transactions in banking to improper insurance claims. And it's not just outsiders perpetrating this fraud. Increasingly, organizations are faced with insider threats, especially in banking where regulations mandate the detection of suspicious trading activity. While there is no shortage of tools aimed at detecting fraud, few can do it cost-effectively at a scale that matters. Many of these tools simply take too long to detect the fraud, hampering efforts to respond.

MapR provides real-time streaming pipelines to analyze each transaction using ML models for fraud as the transaction is happening, even enabling you to prevent the fraud. Only MapR is able to ingest and scale to the trillions of small files - like emails and voice recordings - that are often used as the training data to help detect suspicious insider activity. MapR is the data platform of choice for top-tier banks and insurance companies, not only preventing fraud and meeting regulatory requirements, but ultimately saving on their bottom line.

Modern businesses create huge volumes of data across a variety of sources, in various formats, at extreme speed. Traditional data warehousing solutions fall short when it comes to delivering real-time insights across all operational data. MapR powers real-time operational data hubs that can provide analytics as a service to your internal and/or external stakeholders and applications.

The MapR Data Platform provides schemaless flexibility, high throughput streaming, and built-in ML and BI integrations to provide actionable insights as your business happens. Customers are able to leverage continuous analytics, automated actions, and rapid response to better impact business decisions -- all while avoiding complex ETL processes required to move data from operational systems to analytical systems. The integration of historical and real-time data is available in a single, unified data platform: the MapR Data Platform.

The modern enterprise seeks to create a 360-degree view of their customers. The challenge is in trying to amass and analyze data coming from such diverse sources as social media, support interactions, web, and more. MapR lets organizations combine as many data sources as needed -- at the speed that's required -- to get a complete picture of your customers. Ultimately, organizations reap the benefits by being able to personalize their offers better and reduce churn.

The MapR Data Platform offers a full scale-out architecture, letting you easily grow your customer 360 data set as your customer base grows and as you derive additional datasets to identify upsell opportunities. With secondary indexes, you can find and filter customer data quickly without doing a full table scan. ML can be performed against clickstream data like weblogs and other sources to predict and prevent churn. Finally, customer service reps benefit from fast access to data wherever they might be, as MapR global replication capabilities allows for data to be located as close as possible to where it is used.

Historically, the application of time series data has been limited to server and data center monitoring. Ingest and storage requirements in these cases are large and have mostly been managed by available tools and backend technologies such as OpenTSDB. In the age of IoT, however, organizations now want to apply similar time series analytics against sensor data, but they struggle to find and assemble technologies that can cope with the scale, ingest, and streaming requirements. MapR is an ideal data platform on which to build your time series applications, especially those sourced from sensor data. With a streams-first architecture and scale-out NoSQL database integrated into one converged platform, MapR meets the rigorous technological demands of your time series applications and does it at a lower TCO.

With MapR, a high performance NoSQL database is integrated with a global pub/sub system out of the box to enable real-time data flows. Sensor data, or other time series source data, is captured immediately as a stream, analyzed and processed (optionally by ML tools), and ultimately stored in OpenTSDB with MapR-DB as the backend. MapR is capable of handling ingest rates that exceed 100 million data points per second and is perfectly suited to handle today’s time series needs.

Security tools today are point solutions that address least common denominator situations and cannot scale. These tools have their place, but they are often stitched together and result in missed threats and slower detection capabilities. MapR is your real-time, adaptive, security data fabric. Serving as the foundation for all your security-related data, we allow you to find more threats, faster, in ways that are most meaningful to your organization and environment.

The combination of high-speed ingestion of logs via POSIX, a streams-first architecture, and flexibility in ML let you act on suspicious events in real-time. Additionally, you keep more of your data available via cost-effective, tiered storage, letting you go back in time for forensic purposes or to apply new learnings to old data. With MapR, your security analysts can finally be the data practitioners they need to be.

The General Data Protection Regulation (GDPR), which went into effect May 25, 2018, mandates certain rights for EU residents and their personal data. GDPR stipulates that, upon request, organizations must erase or rectify personal data, allow for portability of personal data, and much more. Though the path to GDPR compliance is rarely associated with analytics initiatives, it should be.

By building a data lake that consolidates personal information across enterprise systems, organizations gain full visibility into what data is being stored about users and, just as importantly, how that data is being used. The MapR Data Platform can scale out to support nearly an unlimited amount of data across diverse data types - files, tables, and streams - giving you a better, more comprehensive view of the user data you are storing. With built-in features like high-performance, streaming audits, and a read-write filesystem that updates user records at 8kb blocks, MapR is an ideal platform for addressing many GDPR requirements.